GeoBurst:*Real.time*Local*Event* - - PowerPoint PPT Presentation

GeoBurst:*Real.time*Local*Event* Detection*in*Geo.Tagged*Tweet*Streams

Chao*Zhang1,*Guangyu*Zhou1,*Quan*Yuan1,*Honglei*Zhuang1,** Yu*Zheng2,*Lance*Kaplan3,*Shaowen*Wang1,*Jiawei*Han1*

1UIUC* 2Microsoft*Research* 3U.S.*Army*Research*Lab

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What*is*a*Local*Event?

• A*local*events*is*an*unusual&activity*bursted*within*a*local&

area*and*specific&duration*while*engaging*a*considerable* number*of*participants.*

• E.g.,*parade,*riot,*sport*game,*concert,*accident,*disaster.

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Local*Event*Detection

• Real.time*local*event*detection*is*important*for*various*

applications*

• disaster*monitoring*
• crime*alarming*
• activity*recommendation*
• …

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Why*Geo.Tagged*Tweet*Stream?

• Real.time*local*event*detection*is*nearly*impossible*years*ago*

due*to*the*lack*of*timely*and*reliable*data*sources.**

• The*geo.tagged*tweet*stream*brings*new*opportunities*to*

this*problem*because*of*its*(1)*sheer*size;*(2)*multi. dimensional*information;*and*(3)*real.time*nature.

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Our*Goal

• Given*the*geo.tagged*tweet*stream,*we*aim*to**
• detect*all*local*events*in*any*query*time*window*(batch&mode);*
• update*the*result*list*in*real*time*as*the*query*window*shifts*

continuously*(online&mode).

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query window Q time

Challenges

1.Integrate&multiple&types&of&data.&

• Location,*time*and*text*have*totally*different*representations.*

2.Extracting&interpretable&events&from&massive&noise.&

• Raw*tweets*are*extremely*noisy*and*short.*

3.&On>line&and&real>time&detection.&

• To*allow*for*timely*actions,*local*events*should*be*detected*in*real*

time.

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Previous*Studies

• Most*existing*event*detection*methods*are*designed*for*

detecting*global&events*

• They*can*successfully*detect*events*that*are*bursty*in*the*entire*

stream;*

• But*local*events*are*“bursty”*in*a*small*region*and*involve*a*limited*

number*of*tweets.*

• A*few*methods*for*local*event*detection*have*been*proposed*
• They*either*do*not*model*the*correlations*between*keywords;*or*

are*incapable*of*detecting*local*events*in*real*time.

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2011 ICWSM. Event detection in twitter. 2012 CIKM. Twevent: segment-based event detection from tweets. 2009 CIKM. Event detection from Flickr data through wavelet-based spatial analysis. 2013 PVLDB. EventTweet: Online localized event detection in the twitter stream.

Our*Insight

• A*local*event*usually*leads*to*many*related*tweets*around*the*

location*(a&geo>topic&cluster).*

• But*a&geo>topic&cluster&is&not&necessarily&a&local&event:*
• It*may*be*a*routine*activity*in*that*region*(e.g.,*shopping).*
• It*may*be*a*global*event*rather*than*a*local*one*(e.g.,*TV*show).

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We define a local event as a geo-topic cluster that shows clear spatiotemporal burstiness.

Overview*of*GeoBurst

• We*propose*GeoBurst,*a*reference.based*method*for*local*

event*detection.*It*consists*of*three*key*components:*

• a&candidate&generator*that*finds*geo.topic*clusters*in*the*query*

time*frame,*and*regard*them*as*candidate*events;*

• a&ranking&module*that*summarizes*the*routine*activities*in*different*

regions*to*filter*non.event*candidates.*

• an&updater*that*updates*local*events*in*real*time*as*the*query*

window*shifts.

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Candidate*Event*Generation

• The*candidate*generator*finds*geo.topic*clusters*in*the*query*

time*frame*as*candidate*events.*

• Geo.topic*cluster:*a*group*of*tweets*that*are*geographically*

close*and*semantically*relevant.*

• Challenges*for*finding*geo.topic*clusters:*
• How*to*combine*geographical*and*semantic*similarities?*
• How*to*capture*the*correlations*between*different*keywords?*
• How*to*cluster*without*knowing*the*number*of*clusters*in*advance?

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Candidate*Event*Generation

• Intuition:*the*spot*where*the*event*occurs*is*acting*as*a*pivot*

that*produces*relevant*tweets*around*it.*

• Our*clustering*algorithm*is*based*on:*
• a*geo.topic*authority*score*for*each*tweet*
• an*authority*ascent*process*to*find*authority*maxima*as*pivots

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Geo.topic*Authority

• A*tweet*gets*an&authority&score*from*neighbor*tweets*where*
• the*geographical*impact*is*captured*by*kernel*function;*
• the*semantic*impact*is*captured*by*random*walk*on*the*keyword*

co.occurrence*graph.

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authority

music, show music band music, band music shop A B C D E

geo-impact semantic impact

Authority can be interpreted as the total amount of energy received from the neighbors.

Pivot

• A*pivot*is*an*authority*maximum:*a*prominent*tweet*that*is*

surrounded*by*many*relevant*tweets.

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music, show music band music, band music shop A B C D E

Authority*Ascent

• Now*the*task*is*to*find*all*the*pivots*in*the*geo.topic*space.*
• We*design*an*authority&ascent*process*to*find*all*pivots.*
• A&pivot&attracts&similar&tweets*to*form*geo.topic*clusters.

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neighborhood

d1 d2 d3

neighbor local pivot pivot

The*Ranking*Module

• We*design*the*activity&timeline&structure*to*summarize*the*

activities*in*different*spatial*regions*and*time*periods.**

• The*summaries*in*the*activity*timeline*serve*as*background*

knowledge*to*quantify*the*spatiotemporal*burstiness*of* candidates.

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time snapshot activity timeline

Each snapshot is a set of micro-clusters. Each cluster is an activity summary for a region.

The*Ranking*Module

• Retrieve*the*snapshots*in*a*reference*window*as*background*

knowledge.*

• Compute*z.score*for*each*candidate*as*its*ranking*score.*

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The*Update*Module

• In*the*entire*process*of*GeoBurst,*the*most*time.consuming*

step*is*pivot*finding.**

• How*to*avoid*finding*pivots*from*scratch*as*the*query*

window*shifts?*

• The*key*is*to*maintain*the*local*pivot*for*each*tweet.

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neighborhood

d1 d2 d3

neighbor local pivot pivot

The*Update*Module

• We*design*an*updating*strategy*based*on*the*additive*

property*of*authority*score:*

• subtracting*the*contributions*of*outdated*tweets*
• emphasizing*the*contributions*of*new*tweets.

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neighborhood

d1 d2 d3

neighbor local pivot pivot

Experimental*Settings

• Data:**
• NY:*9M*geo.tagged*tweets*in*New*York*during*3*months.*
• LA:*8M*geo.tagged*tweets*in*Los*Angeles*during*3*months.*
• Task:*80*queries*with*different*durations*(3h,*4h,*5h,*6h),*find*

top.5*local*events*in*each*query*window.*

• Compared*Method:*EvenTweet*(PVLDB’13),*Wavelet*(CIKM’09)*
• Evaluation:*The*crowdsourcing*platform*CrowdFlower*
• Ask*the*workers*to*judge*whether*the*result*is*a*local*event*or*not.

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Illustrative*Cases

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Precision

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Running*Time

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• 1. GeoBurst*is*more*efficient*than*the*compared*methods*even*when*in*batch*mode.*

2.The*online*mode*of*GeoBurst*is*more*efficient.

Summary

• We*study*the*problem*of*detecting*local*events*from*the*geo.tagged*

tweet*stream.*

• We*proposed*the*GeoBurst*method.*
• It*first*detects*candidate*events*based*on*authority*ascent,*and*then*ranks*the*

candidates*based*on*background*knowledge.*

• It*also*features*an*updating*module*to*continuously*monitor*the*stream.*
• Experiments*demonstrate*the*effectiveness*and*efficiency*of*GeoBurst.*
• For*future*work,*we*plan*to*extend*GeoBurst*to*handle*the*tweets*that*

mention*geo.location*names*but*do*not*have*GPS*information.

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Thanks!

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